Hostname: page-component-848d4c4894-8kt4b Total loading time: 0 Render date: 2024-06-17T13:09:56.384Z Has data issue: false hasContentIssue false

3184 Development of a Leadership Assessment Scale in Translational Science

Published online by Cambridge University Press:  26 March 2019

Roger Vaughan
Affiliation:
Rockefeller University
Rhonda G Kost
Affiliation:
Rockefeller University
Donna Brassil
Affiliation:
Rockefeller University
Michelle Romanick
Affiliation:
Rockefeller University
Barry S. Coller
Affiliation:
Rockefeller University
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

OBJECTIVES/SPECIFIC AIMS: To create the instrument, we employed a modified Delphi approach by conducting a thorough literature review on Leadership to help concretize the relevant constructs, and then usied these extracted constructs as a springboard for the Rockefeller Team Science Educators (TSE’s) to discuss and refine the leadership domain areas, collectively creating domain-specific survey items, and then further discussed and refining the number, grouping, and wording of the items. METHODS/STUDY POPULATION: We piloted the Leadership Survey by having all of the Rockefeller TSEs rate Clinical Scholars. Each item was answered using a six-point Likert scale where a low score indicated poor expression of the specific leadership attribute and a high score represented excellent expression of the specific leadership attribute. RESULTS/ANTICIPATED RESULTS: Means, medians, standard deviations, and ranges of each item were calculated and tabulated. A complete (Pearson) correlation matrix was computed so that the raw inter-item relationships can be observed. For each a priori Domain an equal weighted summary scale was created and tabulated for review. The internal consistency of each a priori scale was assessed by calculating Cronbach’s Alpha (α). Items with low Item to Construct coefficients were candidates for elimination or modification, and overall scales with low’s will undergo further discussion. To challenge our assumptions of the construction and integrity of each domain, we employed exploratory Principal Components Analysis (PCA), followed by orthogonally rotated Factor Analysis (FA). We also forced the PCA / FA analysis to extract the a priori dimensions that allowed us to compare if the empirical and a priori structures match. DISCUSSION/SIGNIFICANCE OF IMPACT: We are partnering with the CTSA programs at Penn and Yale to assess issues of generalizability and scalability. We are working with Vanderbilt to install survey onto REDCap for ease of dissemination. Will continue to assess psychometric properties and refine as we receive more input.

Type
Team Science
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-ncnd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Association for Clinical and Translational Science 2019